点击次数：

影响因子：4.0

DOI码：10.3389/fphys.2023.1233341

所属单位：Xi'an Jiaotong University; Leiden University; University of Bremen

发表刊物：Frontiers in Physiology

刊物所在地：Switzerland

关键字：minimum spanning tree; outlier detection; cluster-based outlier detection; data mining; medical data

摘要：As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.

备注：ESI Hot Paper (top 0.1%) and Highly-Cited Paper (top 1%).

全部作者：Jia Li, Jiangwei Li, Chenxu Wang, Fons J. Verbeek*, Tanja Schultz, Hui Liu*

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：14

页面范围：1233341

ISSN号：1664-042X

是否译文：否

发表时间：2023-10-13

收录刊物：SCI

发布刊物链接：https://www.frontiersin.org/articles/10.3389/fphys.2023.1233341